Visual cluster analysis and pattern recognition methods

Abstract

A method of clustering using a novel template to define a region of influence. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques.

What is claimed is:  
     
       1. A pattern recognition method, comprising: 
       (a) assigning vector coordinates to each training data point of a training data set having class assignments, and assigning vector coordinates to each test data point of a test data set to be assigned classes;  
       (b) determining class assignments of each test data point of said test data set by clustering those points individually with said training data set, said clustering accomplished by means of:  
       i) selecting each of said test data points of said test data set and selecting each of said training data points of said training data set, and  
       ii) placing a test data point and a training data point on each of two specified positions of a region of influence that is oblong having a central portion narrowed by two opposing concave depressions with two wider larger end portions on both sides of said central portion;  
       (c) assigning said test data point to a class associated with said training data point if no other training data point of said training data set lies within said region of influence;  
       (d) assigning said test data point to an ambiguous class if said test data point groups with no class or more than one class in said training set; and  
       (e) repeating steps (b), (c), and (d) above for said training data set and said test data set.  
     
     
       2. A method to verify quality of a training data set used to make class assignments of data in pattern recognition, comprising: 
       (a) assigning vector coordinates to each data point of a data set having predetermined class assignments;  
       (b) clustering said data points of said data set by placing two of said data point on each of two specified positions of a region of influence that is oblong having a central portion narrowed by two opposing concave depressions with two wider larger end portions on both sides of said central portion;  
       (c) assigning a same class to said clustering data points if no other training data point of said data set lies within said region of influence;  
       (d) assigning said test data point to an ambiguous class if said test data point groups with no class or more than one class in said training set;  
       (e) assigning an ambiguous class if any other data point besides said clustering data points lies within said region of influence;  
       (f) repeating steps (b), (c), and (d) above for said data set.